Palmprint and Finger Knuckle Based Person Authentication with Random Forest via Kernel-2DPCA

This paper presents a hand biometric system by fusing information of palmprint and finger knuckle to check the loopholes that are present in transfer of payments through various levels of bureaucratic financial inclusion projects. Initially, a novel, fixed size ROIs of palm and finger knuckle has been extracted. The poor contrast ROI images are enhanced using modified CLAHE algorithm. To minimize the pose and illumination effects, Line Ordinal Pattern (LOP) based transformation scheme has been applied. The generation of dense feature representation by using dual tree complex wavelet transform can increase the discrimination power of independent local features. Then, the original feature space is mapped into high dimensional sub feature set, where K2DPCA is performed on each subset to extract high order statistics. Addressing to the matching problem, a high-performance Random Forest method has been employed. Finally, the two modalities are combined at weighted sum score level fusion rule which has shown the increased performance (CRR (100%), EER (0.68%), and (computation time (2130 ms)) of combined approach. The proposed method is evaluated using a virtual combination of publicly available PolyU palm print and PolyU FKP databases.